Tensor

Value Members

1. final def !=(arg0: AnyRef): Boolean

   Definition Classes

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

   Definition Classes

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. def apply(matrix: DenseMatrix): Tensor[Double]

   create a tensor with a given spark Densematrix.

   create a tensor with a given spark Densematrix. The tensor will have the same size with the given spark Densematrix.

   matrix

   returns

7. def apply[T](matrix: DenseMatrix[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

   create a tensor with a given breeze matrix.

   create a tensor with a given breeze matrix. The tensor will have the same size with the given breeze matrix.

   T

   matrix

   the given breeze matrix

   ev

   returns

8. def apply(vector: DenseVector): Tensor[Double]

   create a tensor with a given spark Densevector.

   create a tensor with a given spark Densevector. The tensor will have the same size with the given spark Densevector.

   vector

   the given spark Densevector

   returns

9. def apply[T](vector: DenseVector[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

   create a tensor with a given breeze vector.

   create a tensor with a given breeze vector. The tensor will have the same size with the given breeze vector.

   T

   vector

   the given breeze vector

   ev

   returns

10. def apply[T](other: Tensor[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    create a tensor with a given tensor.

    create a tensor with a given tensor. The tensor will have same size with the given tensor.

    T

    other

    the given tensor

    ev

    returns

11. def apply[T](storage: Storage[T], storageOffset: Int, size: Array[Int] = null, stride: Array[Int] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Returns a tensor which uses the existing Storage storage, starting at position storageOffset (>=1).

    Returns a tensor which uses the existing Storage storage, starting at position storageOffset (>=1). The size of each dimension of the tensor is given by the optional Array size. If not given, the size will be computed as the length of storage. The jump necessary to go from one element to the next one in each dimension is given by the optional Array stride. If not given, the stride() will be computed such that the tensor is as contiguous as possible in memory.

    T

    storage

    storageOffset

    size

    stride

    ev

    returns

12. def apply[T](data: Array[T], shape: Array[Int])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Returns a tensor with the given array and shape

    Returns a tensor with the given array and shape

    T

    data

    the given storage

    shape

    the given shape

    ev

    returns

13. def apply[T](storage: Storage[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Returns a tensor which uses the existing Storage storage.

    Returns a tensor which uses the existing Storage storage.

    T

    storage

    the given storage

    ev

    returns

14. def apply[T](sizes: Array[Int])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Create a tensor on given sizes.

    Create a tensor on given sizes. The tensor size will be the product of sizes

    T

    sizes

    ev

    returns

15. def apply[T](dims: Int*)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Create a tensor on given dimensions.

    Create a tensor on given dimensions. The tensor size will be the product of dims

    T

    dims

    ev

    returns

16. def apply[T](xs: Table)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Create a tensor with a table

    Create a tensor with a table

    xs

    the table contains a multi-dimensional numbers

    returns

    a new Tensor

17. def apply[T](d1: Int, d2: Int, d3: Int, d4: Int, d5: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

18. def apply[T](d1: Int, d2: Int, d3: Int, d4: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

19. def apply[T](d1: Int, d2: Int, d3: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

20. def apply[T](d1: Int, d2: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

21. def apply[T](d1: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Create a tensor up to 5 dimensions.

    Create a tensor up to 5 dimensions. The tensor size will be d1 x d2 x d3 x d4 x d5.

    T

    ev

    returns

22. def apply[T]()(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Returns an empty tensor.

    Returns an empty tensor.

    T

    ev

    returns

23. final def asInstanceOf[T0]: T0

    Definition Classes

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24. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

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    @throws( ... )

25. final def eq(arg0: AnyRef): Boolean

    Definition Classes

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26. def equals(arg0: Any): Boolean

    Definition Classes

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27. def expand[T](tensor: Tensor[T], sizes: Int*): Tensor[T]

    This is equivalent to tensor.

    This is equivalent to tensor.expand(sizes.toArray)

    T

    tensor

    sizes

    returns

28. def expandAs[T](tensor: Tensor[T], template: Tensor[T]): Tensor[T]

    This is equivalent to tensor.

    This is equivalent to tensor.expandAs(template)

    T

    tensor

    template

    returns

29. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

    @throws( classOf[java.lang.Throwable] )

30. def gaussian1D[T](size: Int = 3, sigma: Double = 0.25, amplitude: Int = 1, normalize: Boolean = false, mean: Double = 0.5, tensor: Tensor[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    Returns a 1D Gaussian kernel of size size, mean mean and standard deviation sigma.

    Returns a 1D Gaussian kernel of size size, mean mean and standard deviation sigma.

    size

    sigma

    amplitude

    normalize

    mean

    tensor

    If tensor is set, will discard size, and write result to tensor.

    returns

31. final def getClass(): Class[_]

    Definition Classes

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32. def hashCode(): Int

    Definition Classes

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33. final def isInstanceOf[T0]: Boolean

    Definition Classes

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34. def load[T](path: String): Tensor[T]

35. final def ne(arg0: AnyRef): Boolean

    Definition Classes

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36. final def notify(): Unit

    Definition Classes

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37. final def notifyAll(): Unit

    Definition Classes

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38. def ones[T](sizes: Int*)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    return a tensor of sizes filled with 1.

    return a tensor of sizes filled with 1.

    sizes

    returns

    a tensor

39. def randperm[T](size: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    This is equivalent to DenseTensor.

    This is equivalent to DenseTensor.randperm[T](size)

    T

    size

    ev

    returns

40. def range[T](xmin: Double, xmax: Double, step: Int = 1)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

    This is equivalent to DenseTensor.

    This is equivalent to DenseTensor.range(xmin, xmax, step)

    xmin

    xmax

    step

    returns

41. def repeatTensor[T](tensor: Tensor[T], sizes: Int*): Tensor[T]

    This is equivalent to tensor.

    This is equivalent to tensor.repeatTensor(sizes.toArray)

    T

    tensor

    sizes

    returns

42. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

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43. def toString(): String

    Definition Classes

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44. final def wait(): Unit

    Definition Classes

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    @throws( ... )

45. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

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    @throws( ... )

46. final def wait(arg0: Long): Unit

    Definition Classes

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    @throws( ... )